Thermal and Electrical Fault Diagnosis in Oil–Paper Insulation System: A Comparative Study of Natural Esters and Mineral Oil
Bibliographic record
Abstract
Power transformer insulation systems, composed of liquid and solid insulators, are continuously exposed to thermal and electrical stresses that degrade their performance over time and may lead to premature failure. Since these stresses are unavoidable during operation, selecting effective insulating materials is critical for long-term reliability. In this study, Kraft insulation paper was used as the solid insulator and impregnated with three different liquids: mineral oil and two natural esters (NE1204 and NE1215), to evaluate their stability under simultaneous thermal and electrical stress. The degradation behavior of the oil-impregnated papers was assessed using frequency-domain dielectric spectroscopy (FDS) and Fourier-transform infrared spectroscopy (FTIR), enabling early fault detection. Comparative analyses were conducted to evaluate the withstand capability of each liquid type during operation. Results revealed strong correlations between FTIR indicators (e.g., oxidation and hydroxyl group loss) and dielectric parameters (permittivity and loss factor), confirming the effectiveness of this combined diagnostic approach. Post-aging breakdown analysis showed that natural esters, particularly NE1215, offered superior preservation of insulation integrity compared to mineral oil. Differences between the two esters also highlight the role of chemical composition in insulation performance. This study reinforces the potential of natural esters as viable, eco-friendly alternatives in thermally and electrically stressed applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".